Automatic Fire Detection Research Articles

Automatic Fire Detection System based on Сontour Analysis Video Images

A general structure of an automatic fire detection system and the results of a study video analysis algorithms for detecting based on contour analysis. Given contour extraction processes objects in the image and make decisions about the discovery of fire or the absence thereof. Experimental researches on the video images of the forest area done with the use of the developed structure of the automatic fire detection system.

Material Solutions for Passive Fire Protection of Buildings and Structures and Their Performances Testing

In buildings and in civil engineering structures, both active and passive fire protection are used. Active fire protection includes automatic fire detection and fire suppression systems while the passive fire protection's main purpose is to attempt to contain fires or slower their spread. The aim of fire protection system's usage is to maintain the temperature of the building component (structural steel element, electrical installation) bellow the critical temperature during fire but also is intended to contain a fire in the origin fire compartment for a limited period of time. In this paper the passive fire protection material solutions were described and their action mode explained. Starting with thermal insulation barrier, endothermic building materials including concrete and gypsum and also novel solution based on alkali activated binders. Concrete is considered to be fire protective, however, in some specific cases, dense and low permeable concrete (i.e. high performance concrete) has a tendency to spall in explosive way under fire. Several fires in structures have caused the spalling of concrete elements that jeopardized the structure stability. In this specific case polypropylene fibres (PP) added to the concrete mix act as a passive protection system. Another group of passive fire protection materials, described in this document, are the intumescent and ablative materials for steel structure protection. The present manuscript describes also the techniques of passive fire protection testing in fire conditions.

Benchmarking of wildland fire colour segmentation algorithms

Recently, computer vision-based methods have started to replace conventional sensor-based fire detection technologies. In general, visible band image sequences are used to automatically detect suspicious fire events in indoor or outdoor environments. There are several methods which aim to achieve automatic fire detection on visible band images, however, it is difficult to identify which method is the best performing as there is no fire image dataset which can be used to test the different methods. This study presents a benchmarking of state of the art wildland fire colour segmentation algorithms using a new fire dataset introduced for the first time. The dataset contains images of wildland fire in different contexts (fuel, background, luminosity, smoke etc.). All images of the dataset are characterised according to the principal colour of the fire, the luminosity, and the presence of smoke in the fire area. With this characterisation, it has been possible to determine on which kind of images each algorithm is efficient. Also a new probabilistic fire segmentation algorithm is introduced and compared to the other techniques. Benchmarking is performed in order to assess performances of 12 algorithms that can be used for the segmentation of wildland fire images.

Wireless Sensor Network Based on GSM for Automatic Fire Detection and Rescue System in Train

Wireless Sensor Network Based on GSM for Automatic Fire Detection and Rescue System in Train

IMPROVING EFFICIENCY IN EMERGENCY RESPONSE FOR CONSTRUCTION SITE FIRES: AN EXPLORATORY CASE STUDY

This study proposes an approach to assist construction managers with fire management. Since accidental fires at construction sites often go undetected until they begin to spread, they can cause serious damage. Although researchers have applied information technology to construction site safety management, few studies have focused on the relation­ship between information techniques and construction site fires. After investigating this relationship, the results revealed two problems: difficult fire detection and disorderly emergency response. Therefore, the intent of this study was to build an intelligent construction site fire management platform by integrating image recognition for automatic fire detection and knowledge models for immediate emergency response. Based on several tests on three common types of acciden­tal fires at construction sites, the results demonstrated that the platform can detect fires and alert construction manag­ers quickly. Through mobile information transmission, the construction managers were able to understand the status of on-site fires and respond appropriately. This study offers a useful reference for similar applications in construction site safety management.

Automatic fire pixel detection using image processing: a comparative analysis of rule-based and machine learning-based methods

This paper presents a comparative analysis of state-of-the art image processing-based fire color detection rules and methods in the context of geometrical characteristics measurement of wildland fires. Two new rules and two new detection methods using an intelligent combination of the rules are presented, and their performances are compared with their counterparts. The benchmark is performed on approximately two hundred million fire pixels and seven hundred million non-fire pixels extracted from five hundred wildland images under diverse imaging conditions. The fire pixels are categorized according to fire color and existence of smoke; meanwhile, non-fire pixels are categorized according to the average intensity of the corresponding image. This characterization allows to analyze the performance of each rule by category. It is shown that the performances of the existing rules and methods from the literature are category dependent, and none of them is able to perform equally well on all categories. Meanwhile, a new proposed method based on machine learning techniques and using all the rules as features outperforms existing state-of-the-art techniques in the literature by performing almost equally well on different categories. Thus, this method, promises very interesting developments for the future of metrologic tools for fire detection in unstructured environments.

Colour based fire detection method with temporal intensity variation filtration

Development of video, computing technologies and computer vision gives a possibility of automatic fire detection on video information. Under that project different algorithms was implemented to find more efficient way of fire detection. In that article colour based fire detection algorithm is described. But it is not enough to use only colour information to detect fire properly. The main reason of this is that in the shooting conditions may be a lot of things having colour similar to fire. A temporary intensity variation of pixels is used to separate them from the fire. These variations are averaged over the series of several frames. This algorithm shows robust work and was realised as a computer program by using of the OpenCV library.

An optimal many-core model-based supercomputing for accelerating video-equipped fire detection

Automatic fire detection has become more and more appealing because of the increasing use of video capabilities in surveillance systems used for early detection of fire. However, its high computational complexities limit its use in real-time applications. To meet the real-time processing of today's fire detection techniques, this study proposes a single instruction, multiple data many-core model. To design an efficient many-core model for image processing applications such as fire detection, a key design parameter is the image data-per-processing-element (IDPE) variation of the many-core system, which is the amount of image data directly mapped to each processing element PE. This study quantitatively evaluates the impact of the IDPE variation on system performance and energy efficiency for the multi-stage fire detection approach that consists of movement-containing region detection, color segmentation, fire feature extraction of fires, and decision making if there is a fire or non-fire in a processing video frame. In this study, we use six IDPE ratios to determine an optimal many-core model that provides the most efficient operation for fire detection using architectural and workload simulation. Experimental results indicate that the most efficient many-core model is achieved at the 64 IDPE value in terms of the worst-case execution time and energy efficiency. In addition, this study compares the performance of the most efficient many-core configuration with that of a commercial graphics processing unit (Nvidia GeForce GTX 480) to show the improved performance of the proposed many-core model for the fire detection algorithm. This many-core configuration outperforms the commercial graphic processing unit in the worst-case execution time and energy efficiency.

Research on the Automatic Fire Detection and Fire Suppression Technique Scheme in the Coal Mine

介绍了以基于拉曼散射的分布式光纤温度传感器对煤矿井下火灾进行自动探测的设计，分析了分布式光纤应用于煤矿井下的可行性和优越性，并结合水喷雾技术，共同构成煤矿井下火灾的自动探测及灭火系统。在发生非电气类火灾时，可以用其进行灭火，在平时可以用其对井下进行洒水除尘处理，实现资源的最大化利用。This paper introduced the distributed optical fiber temperature sensor based on Raman scattering for design of automatic fire detection in the underground coal mine. Feasibility and superiority of the distributed optical fiber applied in coal mine was analyzed. Combined with water spray technology, the distributed optical fiber constituted the automatic fire detection and fire extin-guishing system in the underground coal mine. In the event of non-electrical fire, it can be used to put out the fire, and be usually used for underground sprinkling service, which maximizes the use of resources.

A Novel Algorithm for Fire/Smoke Detection based on Computer-Vision

The scarcity of automatic fire detection systems continues to be a problem that needs a serious attention in order to save human lives by preventing injuries and/or deaths. The newest innovations are continuing to use cameras and computer algorithms to analyze the visible effects of fire and its motion in their applications. As their approaches present some drawbacks when working in spatial domain, the main difficulty is still to identify objects if they do not occur at the expected position. In this paper, we present an improved fast and robust algorithm for detecting fire/smoke in a cluttered scene from a pair of cameras. The input images are first segmented according to a pre-determined disparity threshold map and the real-time disparities of fire. Binary image processing techniques are used to reject noise introduced into the segmented images through low-resolution disparity calculations which consequently can lead to the gain of clearer results. In order to reduce the false alarms, a new segmentation method used in this approach shows that segmented images using stereovision are more accurate than those segmented using color approach for the overall detection. The segmented images are then used for image feature extraction for a Fuzzyneural network classifier to help the system to generate a warning in case fire/smoke is detected.

IEEE 1451-Based Sensor Interfacing and Data Fusion for Fire Smoke Detection

The performance of a fire sensor has a significant effect on fire detection. Today’s fire alarm systems, such as smoke and heat sensors, however are generally limited to a close proximity to the fire; and cannot provide additional information about fire circumstances. Thus, it is essential to design a suite of low-cost networked sensors that provide the capability of performing distributed measurement and control in real time. In this work, a wireless sensor system was developed for fire detection. The purpose of this paper is to analyze the integration of traditional fire sensors into intelligent fire management systems by using the smart transducer concept. An automated video processing sensor for fire smoke monitoring applications is integrated into an surveillance network as a case study and supported sensor fusion assessment to improve the resistance to nuisance alarms. The proposed sensor system for fire detection was developed to reconcile issues related to proliferation and interoperability, and the architecture can support a smart transducer interface (IEEE 1451). The proposed embedded system for STIM (smart transducer interface module) and NCAP (network capable application processor) will be implemented with DSP. To realize the self-identification of transducers and plug-and-play connections, a transducer electronic data sheet (TEDS) is also stored inside the DSP. The acquired sensor data are pre-processed and applied to discriminate nuisance sources. The IEEE 1451 standard has been integrated into an automatic video-based fire smoke detection system. The proposed architecture has been tested on an experimental setup with the purpose of monitoring fire incidents successfully.

국내 공동주택 화재감지시스템의 성능개선을 위한 기초연구

This study examines the performance of and searches for improvements to the existing automatic fire detection systems installed at domestic apartment buildings as a basic study for development of intelligent fire detection systems specifically for apartments. Thus, this study aims to find out the problems in performance and maintenance of the existing fire detectors installed at apartment buildings which is the prerequisite process for development of intelligent fire detection system for that specific application. It is also found impossible to check whether or not the detectors installed at each apartment are in an operational state at normal times. This study finds that it is desirable to replace the slow-sensing heat detectors by a smoke and single smoke detectors which can detect a fire at its early stage in an effort to improve the problems of fire detectors installed at apartment buildings presently. Because we need to the independence fire detection system of apartment building.

Detection and mapping of burnt areas from time series of MODIS-derived NDVI data in a Mediterranean region

AbstractModerate resolution remote sensing data, as provided by MODIS, can be used to detect and map active or past wildfires from daily records of suitable combinations of reflectance bands. The objective of the present work was to develop and test simple algorithms and variations for automatic or semiautomatic detection of burnt areas from time series data of MODIS biweekly vegetation indices for a Mediterranean region. MODIS-derived NDVI 250m time series data for the Valencia region, East Spain, were subjected to a two-step process for the detection of candidate burnt areas, and the results compared with available fire event records from the Valencia Regional Government. For each pixel and date in the data series, a model was fitted to both the previous and posterior time series data. Combining drops between two consecutive points and 1-year average drops, we used discrepancies or jumps between the pre and post models to identify seed pixels, and then delimitated fire scars for each potential wildfire using an extension algorithm from the seed pixels. The resulting maps of the detected burnt areas showed a very good agreement with the perimeters registered in the database of fire records used as reference. Overall accuracies and indices of agreement were very high, and omission and commission errors were similar or lower than in previous studies that used automatic or semiautomatic fire scar detection based on remote sensing. This supports the effectiveness of the method for detecting and mapping burnt areas in the Mediterranean region.

영상기반 자동 화재감지시스템의 감지성능 특성 분석 연구

기존의 연기감지기와 불꽃감지기는 화재 발생시 주변 환경의 영향에 의해 감지성능이 크게 제한되어 명확한 화재감지에 문제점을 가지고 있다. 본 연구에서는 화재감지 성능을 향상시키기 위하여 CCTV 카메라, 영상화재수신기, CPU로 구성된 영상기반 자동 화재감지시스템의 감지성능을 분석하고자 한다. 지능형 모션 감지를 위한 GMM(Gaussian Mixture Model) 모델링이 해석되었고 MHI(Motion History Image) 기술의 적용은 화재감지 성능을 향상시킴을 보여 주었다. 화재감지 성능은 화원과 감지거리에 따라 실험적으로 평가되었다. 결과적으로, 화재감지 시간은 감지거리가 증가할수록 증가하며 감지거리 15 m에서 10초로 분석되었다. 따라서, 개발된 영상기반 자동 화재감지 시스템은 불꽃감지기의 감지시간기준 30초와 비교하여 자동 화재감지를 위한 양호한 성능을 보여 주었다. Smoke detectors and flame detectors in detection ability of sensors is greatly limited by the ability to detect environmental impacts in case of fire have a problem in accurate fire detection. This study describes to analyze detection performance of a video-based automated fire detection system consisted of CCTV camera, a video-based fire alarm control station, CPU in order to enhance the fire detection performance. GMM(Gaussian Mixture Model) modeling for an intelligent motion detection is analyzed and the application of MHI(Motion History Image) technology is showed the improved fire detection performance. The fire detection performance is experimentally evaluated as a function of the fire source and detection distance. As a result, the fire detection time is found to increase with increased the detection distance and is analyzed with 10 sec at the detection distance of 15 m. Therefore, the video-based automated fire detection system developed showed good performance for automatic fire detection comparing with the detection time criteria of 30 sec for flame detectors.

Analysis of Dust Properties to Solve the Complex Problem of Non-fire Sensitivity Testing of Optical Smoke Detectors

The purpose of an automatic fire detection system is the fast and reliable detection of arising fires in order to keep damage as low as possible. The European Standard EN54 defines a series of tests to prove and certify the functionality of smoke detectors, i.e. to prove that the detector is able to detect a fire in a prescribed period of time. Unfortunately the complex task of avoiding false alarms is not completely addressed. In contrast to the well standardized methods for the evaluation of the detection capability of a smoke detector, there is a lack of a reproducible and representative test method concerning the false alarm susceptibility with regard to nuisance aerosols. The consequences of false alarms should not be underestimated, as they may cause costs to serve the operator, especially in airborne applications. Many false alarms are caused by construction works in the surrounding of smoke detectors. For that reason several dust sources have been analyzed. Several approaches are possible and have been implemented to reduce the false alarm susceptibility of optical smoke detectors caused by dust and steam, e.g. different wavelengths and scattering angles. Unfortunately the developer has no representative test methods to quantify improvements and to point out the false-alarm resistance with e.g. a seal of quality due to new developments. Important is the knowledge of dust and steam properties such as the particle size distribution in comparison to particle size distribution of smoke of a fire. This paper presents a new approach for the test of smoke detectors regarding their susceptibility to false alarms due to nuisance aerosols, like steam and dust. The presented test apparatus is a very helpful and important tool for developers as well as for test houses during the developing and certification process. System designer will have a quantitative decision criterion to find the optimal detector for a specific place of installation. The paper compares the analysis of dust properties caused by construction works with standardized test dusts and shows how to solve the problem.

A Study on Fire Detection System using Statistic Color Model

Normally fire detection system uses the heuristic fixed threshold values in their specific methods. However, input images may be changed, in general, so the heuristic fixed threshold values used in the fire detection systems might be modified on a case by case basis. In this paper, an automatic fire detection system without the heuristic fixed threshold values was studied. We presented an automatic method using the statistical color model and the binary background mask. We did the experiment using 600 frames from 6 typical different fire video clips. As the experimental results the proposed method showed a good performance of about average 85% detection rate without false positive, compared with the other methods with the heuristic fixed threshold values.

An Overview of Fire Risk and Fire Protection Solutions for Wind Turbines

To search effective technical solutions to control fire risk of wind turbines, based on the risk system composed of “Human- Equipment - Environment – Management” in safety systems engineering theory, the four main aspects of wind turbines fire risk, i.e., lightning strikes and bad weather, mechanical and electrical equipment failure, human errors and poor management and fire protection systems missing, were pointed out. The fire risk and fire risk control measures of wind turbines were analyzed, the progress of fire detection technology and fire protection solutions of wind turbines were reviewed. The results show that, to develop and equip with high-performance automatic fire detection, alarm and extinguishing systems is the inevitable choice to effectively control the fire risk of wind turbines.

Automatic fire detection based on soft computing techniques: review from 2000 to 2010

Automatic fire detection system is a system that is capable of assessing environmental factors and their effects on the environment as well as predicting the occurrence of fire in the early stages and even before the outbreak. There are two perspectives in fire detection: fire detection in forests or jungles and fire detection in occupied or residential areas. Automatic fire detection has attracted increased attention due to its importance in decreasing fire damage. There are many studies that have considered appropriate techniques for early fire detection. In recent years researches have been studying technical developments in this field aimed at exploiting wireless communications networks, detection systems and fire prediction systems design. In this paper the automatic fire detection researches using intelligent techniques from 2000 to 2010 is reviewed. We could classify researches to four categories: fire detectors, reduce false alarms systems, fire data analysis and fire predictors. We also classify the intelligent techniques outlined in the researches for each category.

양방향 통신이 가능한 자동화재탐지설비(P형 1급 수신기)의 설계 및 동작특성에 관한 연구

In this paper, authors will develop the quick and precise remote controller of automatic fire detection equipment (P-type one-class receiver) based on information communication technology (IT). The remote controller detects the fire and disaster in the building automatically and quickly and then activates the facilities to extinguish the fire and disaster, monitoring such situation in a real time through wire-wireless communication network. The proposed remote controller is applied a programmable logic device (PLD) micom. of one-chip type which is small size and lightweight and also has highly sensitive-precise reliabilities. The one-chip type PLD micom. analyzes digital signals from sensors, then activates fire extinguishing facilities for alarm and rapid suppression in a case of fire and disaster. The detected data is also transferred to a remote situation room through wire-wireless network of RS232c and bluetooth communication, and then the situation room sends an emergency alarm signal. The automatic fire detection equipment (AFDE) based on IT will minimize the life and wealth loss while prevents fire and disaster.

Study on Source Locating Technology of Visible Fire Detection in Large Space Building

Locating fire source position is a significant aspect of automatic fire detection since it plays an important role in extinguishing fire in large space building in a short time. On the basis of previous literatures of visible fire location detection through analyzing the features of fire images, binocular vision based fire source spatial location is proposed in accordance with the computer vision principle in this paper. The binocular vision location algorithm is employed to identify the fire source position. To reach this, three typical spatial positions of fire source are measured in terms of camera scanning firstly; Secondly, confidence coefficients are added into the measured data to obtain the accurate position of fire source. Finally, the amount of water in cannon is adjusted to the right direction to put out the fire.